Abstract: In response to the increased demand for individualized workout routines, online aerobics programs are struggling to fulfil the needs of their various user bases with specialized suggestions. Current systems seldom combine multiple data sources to analyze user preferences, reducing customization accuracy and engagement. Enhanced BERT-Residual Network (EBRN) evaluates multimodal input using residual processing blocks and contextual embeddings based on BERT to bridge textual and structural user characteristics. EBRN’s deep insights may help understand user engagement, fitness goals, and enjoyment. An innovative data balancing and feature selection method, Dynamic Equilibrium Sampling and Feature Transformation (DES-FT), improves data preparation and model accuracy. Two novel metrics, Contextual Scheduling Consistency (CSC) and Complexity-Weighted Accuracy (CWA), may quantify EBRN stability in multi-attribute classification, particularly for complex data. EBRN outperforms standard AI models on a Toronto fitness platform dataset with 98.7% recall, 98.9% precision, and 99.3% accuracy. Its limited geographical dataset and lack of real-time validation hinder the research. The data show individualized aerobics recommendations that include instructor quality, platform accessibility, and material variety may boost involvement. Researchers need additional datasets and real-time flexibility to make this concept more practical. EBRN’s tailored ideas revolutionized digital fitness platform user engagement and enjoyment.

Yan Hu, “Multi-Label Decision-Making for Aerobics Platform Selection with Enhanced BERT-Residual Network” International Journal of Advanced Computer Science and Applications(IJACSA), 15(12), 2024. http://dx.doi.org/10.14569/IJACSA.2024.0151288

@article{Hu2024,
title = {Multi-Label Decision-Making for Aerobics Platform Selection with Enhanced BERT-Residual Network},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.0151288},
url = {http://dx.doi.org/10.14569/IJACSA.2024.0151288},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {12},
author = {Yan Hu}
}